这篇论文把知识图谱和可解释AI怎么配合讲清楚了，还给出了四种具体集成模式，干城市矿产评估的可以看看。
本文针对城市矿产预拆除评估中可辩护性需求，提出知识图谱（KG）与可解释AI（XAI）互补解释框架。基于IS资源传统，归纳四种KG-XAI集成模式：Lifting、Constraining、Typing、Revising，每种模式解锁不同的可辩护性属性。以防火门示例展示了基于W3C Linked Building Data栈和估值扩展的具体应用，论述为何单一资源无法满足监管审计要求。
Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
Pre-demolition assessment, the regulated audit process at the heart of urban mining, is an information process in which AI support must serve qualified auditors who remain accountable for the decisions taken. The relevant unit of value is not prediction accuracy alone, but the defensibility of the supported decisions: their legibility, plausibility, sourcing, and contestability. Explainable AI techniques and domain knowledge graphs each address parts of this requirement, and existing taxonomies have catalogued their integration. The literature is descriptively rich but structurally under-specified: what remains less developed is a structural account of why specific integrations produce artefacts neither resource can provide alone. This paper offers a complementarity-theoretic interpretation grounded in the IS resource-based tradition. We propose four consolidated KG-XAI integration modes (Lifting, Constraining, Typing, and Revising), each defined as a typed operation over XAI artefacts and knowledge-graph substrate structures. Each mode unlocks a distinct property of defensibility and contributes to the kind of regulatory artefact pre-demolition assessment demands. A fire-door example from the urban-mining process illustrates the modes using the W3C Linked Building Data stack and valuation extensions.